Document database — queries, aggregations, indexes and Node.js drivers — written by Aivasu Tech Institute's mentors. Free to read, structured to actually help you learn.
MongoDB notes by Aivasu Tech Institute cover 27 topics — from mongodb home to mongodb charts — each explained with short definitions, syntax and runnable code examples. They are 100% free, need no signup, and work as quick revision for college exams, MongoDB interviews and Aivasu Tech Institute's mentor-led MongoDB course in Pitampura, Delhi.
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MongoDB is a document database. It stores data in a type of JSON format called BSON.
MongoDB is a document database and can be installed locally or hosted in the cloud.
The MongoDB Query API is the way you will interact with your data.
After connecting to your database using mongosh , you can see which database you are using by typing db in your terminal.
You can create a collection using the createCollection() database method.
There are 2 methods to insert documents into a MongoDB database.
There are 2 methods to find and select data from a MongoDB collection, find() and findOne() .
To update an existing document we can use the updateOne() or updateMany() methods.
We can delete documents by using the methods deleteOne() or deleteMany() .
There are many query operators that can be used to compare and reference document fields.
There are many update operators that can be used during document updates.
Aggregation operations allow you to group, sort, perform calculations, analyze data, and much more.
This aggregation stage groups documents by the unique _id expression provided.
This aggregation stage limits the number of documents passed to the next stage.
This aggregation stage passes only the specified fields along to the next aggregation stage.
This aggregation stage groups sorts all documents in the specified sort order.
This aggregation stage behaves like a find. It will filter documents that match the query provided.
This aggregation stage adds new fields to documents.
This aggregation stage counts the total amount of documents passed from the previous stage.
This aggregation stage performs a left outer join to a collection in the same database.
This aggregation stage writes the returned documents from the aggregation pipeline to a collection.
MongoDB Atlas comes with a full-text search engine that can be used to search for documents in a collection.
By default MongoDB has a flexible schema. This means that there is no strict schema validation set up initially.
The MongoDB Data API can be used to query and update data in a MongoDB database without the need for language specific drivers.
The MongoDB Shell (mongosh) is great, but generally you will need to use MongoDB in your application. To do this, MongoDB has many language drivers.
For this tutorial, we will use a MongoDB Atlas database. If you don't already have a MongoDB Atlas account, you can create one for free at MongoDB Atlas.
MongoDB Charts lets you visualize your data in a simple, intuitive way.
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